Python Tutorial: Customer Segmentation in Python

Hi there. Welcome to this course on customer segmentation in Python. It is a powerful analytics technique to group customers and enable the business to customize their product offering and marketing strategy. For example, we can group the customers by the month of their first purchase, segment by their recency, frequency and monetary values, or run K means clustering to identify similar groups of customers based on their purchasing behavior. You will dig deeper into customer purchasing habits and uncover actionable insights. First, a little bit About Me I am currently leading a data science team for Amazon Devices understanding behavior of Amazon Echo, Kindle, Fire TV and Fire Tablets customers. I have been working in the data analytics world for over 10 years and had experience in E commerce, banking, consulting, finance and other industries. To get the most out of this course, you should be able manipulate data in Python with Pandas library, have a basic level of understanding how to manipulate daytime objects, have a decent foundation on plotting in Python with either matplotlib or Seaborn packages, and have some experience with unsupervised learning. Specifically with K means clustering. We will cover some of them in the class, but we expect that you have some knowledge of each in order to learn the most from this course. Cohort Analysis is a descriptive analytics tool. It groups the customers into mutually exclusive cohorts which are then measured over time. Cohort analysis provides deeper insights than the so called vanity metrics. It helps with understanding the high level trends better by providing insights on metrics across both the product and the customer lifecycle. There are three major types of cohorts. Time cohorts are customers who signed up for a product or service during a particular time frame. Analyzing these cohorts shows the customer's behavior depending on the time they started using the company's products or services. The time may be monthly or quarterly, even daily. Behavior Cohorts are customers who purchase a product or subscribe to a service. In the past, it groups customers by the type of product or service they signed up. Customers who signed up for a basic level service might have different needs than those who signed up for advanced services. Understanding the needs of the various cohorts can help a company design custom made services or products for particular segments. Size Cohorts refer to various sizes of customers who purchase companies products or services. This categorization can be based on the amount of spending in some period of time after acquisition or the product type that the customer spent most of their order amount in some period of time. Now let's look at the main elements of the cohort analysis. The cohort analysis data is typically formatted as a pivot table. The row values represent the cohort. In this case, it's the month of the first purchase and customers are pulled into these groups based on their first ever purchase. The column values represent months since acquisition. It can be measured in other time periods like months, days, days, even hours or minutes. That depends on the scope of the analysis. Finally, the metrics are in the table. Here we have the count of active customers. The first column with Cohort Index 1 represents the total number of customers in that cohort. This is the month of their first transaction. We will use this data in the next lessons to calculate the retention rate and other metrics. Let's look at the table. We can see that the first cohort was acquired in December 2010 and there are 715 customers in it. This value here suggests that the last date we're working with is from December 2011 and there are 38 customers in this cohort. Now let's explore the cohort table with customer counts to get familiar.